Exploring the metastasis-related biomarker and carcinogenic mechanism in liver cancer based on single cell technology

Background

Hepatocellular carcinoma (HCC) is a fatal primary malignancy characterized by high invasion and migration. We aimed to explore the underlying metastasis-related mechanism supporting the development of HCC.

Methods

The dataset of single cell RNA-seq (GSE149614) were collected for cell clustering by using the Seurat R package, the FindAllMarkers function was used to find the highly expression and defined the cell cluster. The WebGestaltR package was used for the GO and KEGG function analysis of shared genes, the Gene Set Enrichment Analysis (GSVA) was performed by clusterProfiler R package, the hTFtarget database was used to identify the crucial transcription factors (TFs), the Genomics of Drug Sensitivity in Cancer (GDSC) database was used for the drug sensitivity analysis. Finally, the overexpression and trans-well assay was used for gene function analysis.

Results

We obtained 9 cell clusters from the scRNA-seq data, including the nature killer (NK)/T cells, Myeloid cells, Hepatocytes, Epithelial cells, Endothelial cells, Plasma B cells, Smooth muscle cells, B cells, Liver bud hepatic cells. Further cell ecological analysis indicated that the Hepatocytes and Endothelial cell cluster were closely related to the cancer metastasis. Subsequently, the NDUFA4L2-Hepatocyte, GTSE1-Hepatocyte, ENTPD1-Endothelial and NDUFA4L2-Endothelial were defined as metastasis-supporting cell clusters, in which the NDUFA4L2-Hepatocyte cells was closely related to angiogenesis, while the NDUFA4L2-Endothelial was related with the inflammatory response and complement response. The overexpression and trans-well assay displayed that NDUFA4L2 exhibited clearly metastasis-promoting role in HCC progression.

Conclusion

We identified and defined 4 metastasis-supporting cell clusters by using the single cell technology, the specify shared gene was observed and played crucial role in promoting cancer progression, our findings were expected to provide new insight in control cancer metastasis.

DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection

BACKGROUND

Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification.

RESULTS

Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilize T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 80% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single-cell sequencing workflow.

CONCLUSIONS

Thus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.

Identification of key microRNAs and the underlying molecular mechanism in spinal cord ischemia-reperfusion injury in rats

Spinal cord ischemia-reperfusion injury (SCII) is a pathological process with severe complications such as paraplegia and paralysis. Aberrant miRNA expression is involved in the development of SCII. Differences in the experimenters, filtering conditions, control selection, and sequencing platform may lead to different miRNA expression results. This study systematically analyzes the available SCII miRNA expression data to explore the key differently expressed miRNAs (DEmiRNAs) and the underlying molecular mechanism in SCII. A systematic bioinformatics analysis was performed on 23 representative rat SCII miRNA datasets from PubMed. The target genes of key DEmiRNAs were predicted on miRDB. The DAVID and TFactS databases were utilized for functional enrichment and transcription factor binding analyses. In this study, 19 key DEmiRNAs involved in SCII were identified, 9 of which were upregulated (miR-144-3p, miR-3568, miR-204, miR-30c, miR-34c-3p, miR-155-3p, miR-200b, miR-463, and miR-760-5p) and 10 downregulated (miR-28-5p, miR-21-5p, miR-702-3p, miR-291a-3p, miR-199a-3p, miR-352, miR-743b-3p, miR-125b-2-3p, miR-129-1-3p, and miR-136). KEGG enrichment analysis on the target genes of the upregulated DEmiRNAs revealed that the involved pathways were mainly the cGMP-PKG and cAMP signaling pathways. KEGG enrichment analysis on the target genes of the downregulated DEmiRNAs revealed that the involved pathways were mainly the Chemokine and MAPK signaling pathways. GO enrichment analysis indicated that the target genes of the upregulated DEmiRNAs were markedly enriched in biological processes such as brain development and the positive regulation of transcription from RNA polymerase II promoter. Target genes of the downregulated DEmiRNAs were mainly enriched in biological processes such as intracellular signal transduction and negative regulation of cell proliferation. According to the transcription factor analysis, the four transcription factors, including SP1, GLI1, GLI2, and FOXO3, had important regulatory effects on the target genes of the key DEmiRNAs. Among the upregulated DEmiRNAs, miR-3568 was especially interesting. While SCII causes severe neurological deficits of lower extremities, the anti-miRNA oligonucleotides (AMOs) of miR-3568 improve neurological function. Cleaved caspase-3 and Bax was markedly upregulated in SCII comparing to the sham group, and miR-3568 AMO reduced the upregulation. Bcl-2 expression levels showed a opposite trend as cleaved caspase-3. The expression of GATA6, GATA4, and RBPJ decreased after SCII and miR-3568 AMO attenuated this upregulation. In conclusion, 19 significant DEmiRNAs in the pathogenesis of SCII were identified, and the underlying molecular mechanisms were validated. The DEmiRNAs could serve as potential intervention targets for SCII. Moreover, inhibition of miR-3568 preserved hind limb function after SCII by reducing apoptosis, possibly through regulating GATA6, GATA4, and RBPJ in SCII.

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

BACKGROUND

Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.

RESULTS

To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.

CONCLUSIONS

Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.